Enhancing wind turbine reliability: A hybrid state-space and generative approach to SCADA-based fault detection

Shaher, A., Khan, N., Mushtaq, Z., Hassan, A., Irfan, M., Alwadie, H., Al Dawsari, Saleh and Rahman, S. 2026. Enhancing wind turbine reliability: A hybrid state-space and generative approach to SCADA-based fault detection. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 42 (1) , 34. 10.23967/j.rimni.2025.10.74232

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Abstract

Wind turbine reliability is essential for the renewable energy sector, as failures in key parts such as gearboxes and main bearings lead to more than $10 billion in downtime and maintenance costs each year. Supervisory control and data acquisition (SCADA) systems can monitor turbines using signals such as vibration, power output, and wind speed; however, applying machine learning to this data type is challenging due to the presence of unbalanced fault types and complex time patterns. Previous research has explored physics-informed deep learning, digital twins, and contrastive learning, achieving noteable fault detection accuracy. However, challenges remain in detecting rare faults, dealing with imbalanced data, combining data sources, and model generalization. This study presents StateSpaceNetWithGen (SS-Gen), a hybrid model integrating state-space modeling for temporal dynamics with generative augmentation for class imbalance. Tested on a 35,000-sample SCADA dataset (2018–2019), SS-Gen achieved high accuracy (≈1.00) and F1-score (≈1.00) on this specific dataset, improving by 33% over baselines. To further validate the strengths of the proposed method, the methodology is validated on a second dataset with different distribution. These results support more reliable and interpretable wind turbine health monitoring and move the field toward stronger physics-informed and federated machine learning solutions.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0213-1315
Date of First Compliant Deposit:	17 April 2026
Date of Acceptance:	19 November 2025
Last Modified:	17 Apr 2026 11:32
URI:	https://orca.cardiff.ac.uk/id/eprint/186460

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